Testing Potential Mitigation Strategies for Injuries in Angled Fish
Bibliographic record
Abstract
Recreational angling is a common and enjoyable activity that many people consider a hobby, lifestyle and/or passion.It does however affect fish populations worldwide.In order to promote sustainable fisheries, anglers should use best practices when returning fish to the water.Possible interventions that could be implemented to enable long term survival and improve welfare outcomes include alterations to angling gear or the use of tactics that could reduce blood loss.The goal of my thesis was to identify strategies to mitigate injuries that arise during recreational fishing events.Chapter 2 investigated if replacing treble hooks on hard plastic lures with single hooks would reduce injuries and fish handling time in three common targeted gamefish species.My data suggested that making these alterations reduced unhooking time for Northern Pike, Smallmouth and Largemouth Bass.Shorter unhooking time was shown to decrease air exposure, fish handling time and reduce injuries associated with long unhooking and handling times.Chapter 3 explored the tactic of pouring carbonated beverages on bleeding fish injuries.I did not find any benefits of using Mountain Dew™, Coca Cola™ or carbonated lake water on bleeding injuries in Northern Pike.This study encourages anglers to return the fish to the water to recover instead of intervening.In both chapters, fish were caught with rod and reels with various lure types.Together, these studies recommend that anglers switch the treble hooks on their hard plastic lures to single hooks and return the fish to the water as soon as possible in order to mitigate injuries that arise during catch-and-release events.am fortunate to have worked under the supervision of Dr. Steven J. Cooke and Dr. Andy J. Danulchuk over the past two years and am thankful for all of their guidance and wise words.Without access to Queen's University Biology Station this work would not have been possible, and I am thankful to have been given the opportunity to complete part of my research there and make friendships of a lifetime.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".